Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1572-8145

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining ; day:11 ; month:5 ; year:2022 ; pages:1-27
Journal of intelligent manufacturing ; (11.5.2022), 1-27

Creator: Nadim, Karim
Ragab, Ahmed
Ouali, Mohamed-Salah

Contributor: SpringerLink (Online service)

DOI: 10.1007/s10845-021-01903-y

URN: urn:nbn:de:101:1-2022071709461769426726

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:23 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Nadim, Karim
Ragab, Ahmed
Ouali, Mohamed-Salah
SpringerLink (Online service)

Other Objects (12)

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Artikel

Valued added taxation and industrial sector productivity: a granger causality approach

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Artikel

Valued added taxation and industrial sector productivity: a granger causality approach

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Artikel

Valued added taxation and industrial sector productivity: a granger causality approach

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

Data-driven dynamic causality analysis of industrial systems using interpretable machine learning and process mining

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Valued added taxation and industrial sector productivity: a granger causality approach

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Valued added taxation and industrial sector productivity: a granger causality approach

Explainable and interpretable machine learning and data mining

Demystifying COVID-19 mortality causes with interpretable data mining

The benefit of interpretable data-driven methods for wake steering applications

Granger causality-based cluster sequence mining for spatio-temporal causal relation mining

Data-driven discovery of interpretable water retention models for deformable porous media

Forward-Looking Process Mining : Data-Driven Simulation

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Metrologically interpretable feature extraction using generative AI for industrial machine vision

Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Data-driven identification of predictive risk biomarkers for subgroups of osteoarthritis using interpretable machine learning

Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

Valued added taxation and industrial sector productivity: a granger causality approach

Related objects

Reset password